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Deborah F. TateDepartment of Health Behavior and NutritionGillings School of Global Public HealthUniversity of North Carolina at Chapel HillChapel Hill, NC 27599USA

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We are honored to have been asked to edit the tenth edition of Present Knowledge in Nutrition. The first edition was published in 1953, and throughout the book’s history its authors have been a “Who’s Who” of nutritional science. The current volume is no exception. With this edition, we aimed to find productive, knowledgeable, and well-known authors to help us provide integrated information on nutrition, physiology, health and disease, and public-health applications – all in one text. This ambitious goal was set for one purpose: to provide readers with the most comprehensive and current information covering the broad fields within the nutrition discipline. Reflecting the global relevance of nutrition, our authors come from a number of countries. It is hoped that this edition captures the current state of this vital and dynamic science from an international perspective.

New to this edition are chapters on topics such as epigenetics, metabolomics, and sports nutrition – areas that have developed significantly in recent years. The remaining chapters have all been thoroughly updated to reflect developments since the last edition. Suggested reading lists are now provided for readers wishing to delve further into specific subject areas.

To make this edition as accessible and continuously relevant as possible, it is available in both print and electronic formats. An accompanying website (visit ) provides book owners with access to an Image Bank of tables and figures as well as to any updates the authors may post to their chapters in the future.

We hope this volume will be a valuable reference for researchers, health professionals, and policy experts, and a useful resource for educators and advanced nutrition students.

A great deal of work and dedication was involved in producing this extensive volume. First and foremost, we thank the authors of the 73 chapters who reviewed and condensed a vast amount of knowledge and literature. Their undertaking was significant, and our gratitude for their dedication cannot be overstated. The editors of the ninth edition, Barbara Bowman and Rob Russell, are thanked for the critical help they provided in the conceptualization of this edition as well as in the author selection. All of the chapters in this edition were externally reviewed by leaders in each chapter’s field; their generous, voluntary assistance was invaluable. We thank the International Life Sciences Institute for continuing to foster the production of Present Knowledge in Nutrition, and we especially thank Allison Worden for her guidance and hundreds of hours of work, and for keeping everything on track.

1

SYSTEMS BIOLOGY APPROACHES TO NUTRITION

JAMES C. FLEET, PhD

Purdue University, West Lafayette, Indiana, USA

Summary

Systems biology is an integrative approach to the study of biology. It integrates information gathered from reductionist experiments and various high-density profiling tools to understand how the parts of the system interact with each other and with other external factors such as diet. The science of nutrition is well suited to a systems biology approach. The tools of systems biology can be applied to settings relevant to nutrition with the goal of better understanding the breadth and depth of the impact that changing nutrient status has on physiology and chronic disease risk. However, there are many challenges to appropriately applying the systems biology approach to nutritional science. Among the challenges are those related to cost, study design, statistical analysis, data visualization, data integration, and model building.

Introduction

Reductionism versus Systems Biology: A Changing Paradigm

Nutrition requires an understanding of disciplines such as physiology, cell biology, chemistry, biochemistry, and molecular biology among others. In contrast to this broad view, we apply reductionist experimental approaches to advance our understanding of specific nutrient functions. However, while these approaches have been useful, significant issues limit their utility. For example, it can be difficult to translate mechanism-focused research in cells into the complex physiology of a whole organism. As a result, biological models developed from reductionist experiments often fail to explain why gene knockout mice studies do not have the expected phenotype (e.g. the facilitated diffusion model used to describe intestinal calcium absorption is being challenged by the results from calbindin D9k and TRPV6 knockout mice (Benn et al., 2008; Kutuzova et al., 2008)). Even after extensive examination of a problem with reductionist approaches, we often find that gaps exist in our understanding. It is clear that re-applying the approaches we have traditionally used to investigate nutritional questions is unlikely to yield a different outcome. Because of this we need new approaches that complement traditional reductionist approaches but which give us a new, broader perspective of how nutrients are influencing human biology. Systems biology is such an approach.

Systems biology has been described as an approach to biological research that combines reductionist techniques with an “integrationist” approach to identify and characterize the components of a system, and then to evaluate how each of the components interacts with one another and with their environment. The goal of the systems biology approach is to integrate many types of information so that you get a more complete view of a system (Kohl et al., 2010). This definition has a flexibility that is very attractive to nutrition. The notion of a “system” can be applied narrowly to a cell, where the parts are individual biochemical and signaling pathways and the “environment” is the growth factors and hormones that regulate these pathways. However, it can be applied more broadly to a person, where the integration relates to the physiologic systems and the “environment” is lifestyle variables such as diet. For example, we know that calcium influences bone metabolism but we know that this relies upon the efficiency of intestinal calcium absorption and renal calcium excretion as well as on hormones produced at various sites (e.g. PTH in the parathyroid gland, 1,25-dihydroxyvitamin D in the kidney). Thus, our understanding of how dietary calcium intake influences bone is enhanced by looking at the interactions between multiple tissues rather than just focusing only on bone.

Systems Biology as Discovery Tool

Systems biology is an approach but within this approach are also three classes of novel tools necessary for a successful systems biology analysis. First, there are the high-density phenotyping platforms that allow simultaneous measurement of whole classes of biological compounds, i.e. omics methods such as genomics, transcriptomics, proteomics, metabolomics, and ionomics (). Next, the information from these platforms must be analyzed to identify the important changes resulting from a treatment. This requires the application of sophisticated statistics. Third, the information must be annotated and integrated with prior knowledge: this is the field of bioinformatics.

Definitions related to systems biology

Term

Description

Genomics

The study of the genomes of organisms including influences of DNA sequence variation on biology and the impact of modifying DNA and histones on DNA function (i.e. epigenomics)

Transcriptomics

The study of transcripts from the genome including messenger RNA and non-coding RNA such as micro RNA

Proteomics

The study of proteins in a biological system including their level, location, physical properties, post-translational modifications, structures, and functions

Metabolomics

The study of the unique chemicals (metabolites) that are produced as a result of cellular processes, e.g. small molecules such as lipids, metabolites of intermediary metabolites

Ionomics

The study of the mineral nutrient and trace element composition of an organism

Next-generation sequencing

High-throughput DNA sequencing technologies that parallelize the sequencing process thereby producing millions of sequences at once

Cluster

A graphical representation of relationships between data based on similarities in their concentrations or changes in concentrations

Pathway

A graphical representation of biological data organized on the basis of accepted relationships (e.g. glycolysis; signaling through the insulin receptor; lipoprotein transport)

Network

A complex graphical representation of biological data that is developed from the experimental data. This will include known relationships (pathways) and new relationships linking pathways

Systems Biology and Omics Tools for Biomarker Discovery

Omics analyses are often used to profile a biological state and then essential elements of the profile are used as a biomarker. Theoretically, the more independent traits one incorporates into a biomarker, the less likely it will be that the biomarker will be influenced by extraneous/confounding factors. To illustrate this point we can look to the field of iron metabolism. Nutritional iron status can be evaluated by measuring serum ferritin (high ferritin = high iron status) but this parameter is confounded by chronic inflammation (high inflammation = high ferritin) that can mask iron deficiency (Wang et al., 2010). The serum levels of other proteins are also affected by the changes in iron status, e.g. hepcidin (high levels = high iron status) and soluble transferrin receptor (low levels = high iron status). Whereas hepcidin is affected negatively by inflammation (Nemeth and Ganz, 2009), transferrin receptor is not (Beguin, 2003). Thus, by simultaneously assessing the serum levels of ferritin, transferrin receptor, and a serum marker of chronic inflammation (e.g. C-reactive protein), one can assess iron status and remove the confounding caused by the inflammation associated with acute or chronic disease. The approach of using omics to identify measurements that can be combined to make an effective biomarker has been applied to the assessment of certain cancers (Sikaroodi et al., 2010) and some argue that this approach may be useful for the assessment of nutrient status or of nutrition-related conditions that have proved resistant to the single marker approach (e.g. micronutrients such as zinc) (Lowe et al., 2009).

Use of Systems Biology to Define New Modes of Regulation by a Nutrient or Metabolic State

A second way to use systems biology is to identify the groups of genes/transcripts/proteins/metabolites coordinately regulated under specific conditions. These groups could be organized within known biological pathways or as random groupings driven by statistical correlation, i.e. networks that expose new relationships not previously recognized from traditional reductionist research.

Understanding the Systems Biology Approach

It is an over-simplification to imply that there is just one way to do systems biology research but this section will attempt to provide a framework for approaching a nutritional research problem from the systems biology perspective (see for a summary of the steps in the framework).

An overview of the steps in a systems biology analysis.

Experimental Design

This is the single most important step of a systems biology research project for several reasons (Allison et al., 2006). First, adequate experimental planning is necessary to focus the research and to use resources efficiently. One will likely need multiple time points to collect data on multiple phenotypes. For example, early time points may be more informative for measurement of direct transcriptional regulation (e.g. using transcriptomics or chromatin immunoprecipitation coupled to high-density DNA sequencing [ChIP-seq]). However, later time points will be more informative for evaluating protein production or changes in metabolism. Second, the use of multiple conditions should be examined so that a broader, more representative view of the regulation can be determined. Work from model systems such as yeast, where a knockout line exists for each of the 6700 yeast genes, shows us that combin­ing transcriptomic analysis for all of these lines permits computer modeling that can reveal new biological relationships and coordination of regulatory processes (Beer and Tavazoie, 2004). Third, sample replication is necessary so that the study has sufficient statistical power to detect biologically important differences between treatments. Finally, the experimental plan should control all extraneous variables so that any changes can be unambiguously attributed to the treatment of interest.

Recently a direct method has been developed to directly determine transcription factor binding sites throughout the genome. This approach starts with a chromatin immunoprecipitation (ChIP) assay where transcription factors are cross-linked to DNA at the site of their binding and the complex is isolated using antibodies to the transcription factor (Collas, 2010). The DNA from the ChIP assay is then used either to probe a genome-wide DNA tiling array (ChIP on chip) or sequenced directly using next-generation sequencing methods (ChIP-seq) (Park, 2009). This approach was recently used to identify 2776 geno­mic positions occupied by the vitamin D receptor (VDR) after treating lymphoblastoid cell lines with 1,25-dihydroxyvitamin D. These VDR binding sites were significantly enriched near autoimmune and cancer-associated genes identified from genome-wide association (GWA) studies (Ramagopalan et al., 2010), suggesting this information will help us understand the relationship between transcriptional regulation and various disease states.

Genetic Mapping and Forward Genetics.

Forward genetics, the measurement of phenotype and then determining the associations with variations in genotype, is an important approach that has been virtually untapped for the study of nutrient metabolism and function. The basic concept for this approach starts with the fact that natural sequence variations exist within the genome (e.g. single nucleotide polymorphisms or SNP, copy number variations or CNV), and that this variation is heritable. To be useful in forward genetics, these genetic variations must also influence phenotypes, e.g. tissue mineral levels or fatty acid oxidation rate. Finally, unlike the rare mutations that underlie various genetic diseases and cause extreme phenotypes (such as the mutations in copper transporting ATPases responsible for Wilson’s and Menkes disease), the phenotypic changes resulting from the natural variation identified by forward genetics are not fatal but could result in extreme differences between individuals. The goal is to use variations in phenotypes that result from controlled breeding strategies or within pedigrees to map the location of the natural genetic variation that controls the phenotype. The forward genetics approach makes no assumptions about the genes that influence the trait. Rather, it lets variations in phenotype direct us to the regions of the genome containing genetic variants that have a significant biological impact. Forward genetics is particularly useful in instances where we don’t know enough about the metabolism of a nutrient to justify making gene knockout or transgenic mice (i.e. use of reverse genetics) or when mice continue to have normal biology when a candidate gene is deleted (e.g. suggesting redundancies in the system that need to be revealed).

Relating nutritionally important phenotypes to natural variation can be accomplished in two ways: gene mapping and gene association. Linkage analysis within large families and quantitative trait loci (QTL) mapping in controlled crosses between genetically well-characterized inbred mouse lines have been traditionally used to correlate the variation in a phenotype to sequence variations in the genome (Flint et al., 2005). More recently researchers have begun using the genome-wide association (GWA) study approach whereby a multitude of individual variants or haplotypes of variants are examined for their association with a nutritionally relevant trait within large populations of free-living individuals (Manolio, 2010). However, some are concerned that the GWAS approach is subject to false positives and that GWAS findings are difficult to replicate. Regardless of which approach one takes, once the genetic region or candidate polymorphism is identified, additional studies must be conducted to identify the genes that contain the variation controlling the trait, and traditional reductionist research must be done to learn how the genes identified are involved in the regulation of the trait. Because forward genetics approaches are unbiased and hypothesis free, they can lead to the identification of new biological roles for genes and their protein products (Flint et al., 2005).

The promise of forward genetics for nutrition was recently demonstrated for iron metabolism (Wang et al., 2007) where the genes controlling 30% of the variation in spleen iron levels between inbred mouse lines were mapped to chromosome 9. Within this locus, variation was identified in the Mon1a gene and this information was used to determine that Mon1a is a critical component of spleen iron uptake and recycling of red blood cell iron within macrophages. Thus, even though we have learned a tremendous amount about iron metabolism over the last 15 years from traditional approaches (Andrews, 2008), forward genetics permitted researchers to add another piece to this already complex picture.

Epigenomics.

In addition to the regulation mediated through DNA sequences, DNA and histones can be modified and this will influence gene transcription (Mathers, 2008) (also, see Chapter 2). In humans, DNA is organized into a nucleosome complex with four histone proteins (H2A, H2B, H3, and H4). The amino terminal tails of the histones can be post-translationally modified in a variety of ways. Histone acetylation reduces histone association with DNA and is permissive for gene transcription whereas histone methylation is associated with both transcription repression and activation. DNA can also be methylated at the C5 position of cytosine in regions of DNA called CpG islands. Cytosine- and guanine-rich sequences are located near coding sequences in about 50% of mammalian genes. When the CpG islands are methylated, these regions become more compact and this prevents gene transcription (Attwood et al., 2002). DNA methylation is responsible for X chromosome inactivation, genomic imprinting, and tissue-specific gene transcription that occur during cellular differentiation.

Because of its importance in gene repression, researchers have developed DNA microarrays and next-generation DNA sequencing approaches for epigenetic profiling of CpG islands in the human genome (Fouse et al., 2010). Folate and other micronutrients are involved in the production of the universal methyl donor S-adenosyl methionine, so it has been proposed that dietary inadequacy may have a global influence on DNA methylation (Oommen et al., 2005). However, the evidence for this diet-induced regulatory paradigm is not yet secure.

Transcriptomics

It is now possible to simultaneously measure the primary transcripts and all of the alternatively spliced forms of transcripts produced from each gene in the genome of humans and several model organisms (i.e. the transcriptome). Transcript levels are a reflection of both primary regulation by a treatment and secondary regulation that follows from the initial regulatory events (). There are many high quality options from assessing the transcriptome including spotted cDNA arrays, tiling oligonucleotide arrays, and even direct sequencing of RNA (Kirby et al., 2007; Forrest and Carninci, 2009). Many factors can influence the choice of a transcript profiling platform including cost, reproducibility of results, breadth of transcript coverage, and availability. Readers should consult other reviews for additional discussion of the strengths and weaknesses of various array platforms (Hoheisel, 2006; Kawasaki, 2006).

A schematic demonstrating the interactions between the various levels of regulation within a cell. Regulatory events occur at the level of transcription (gene space), RNA translation (protein space), protein stability or interactions (protein space), and protein function (gene space, metabolic space). Inorganic elements are involved in all of these processes (ionomic space), and events occurring in one regulatory space can influence events occurring within another regulatory space (e.g. lipid metabolites bind to protein transcription factors with zinc finger DNA binding domains such as PPAR gamma and this interaction regulates gene transcription). Systems biology attempts to model these complex interactions. Reduced omic phenotyping (e.g. in just the gene space or the metabolic space) – combined with previously published research findings – can be used to infer regulatory interactions between these levels.

For some nutrients that are known to have a direct impact on gene transcription through the activation of a nuclear receptor (e.g. vitamin D and VDR, vitamin A and the retinoic acid receptor bioactive lipids and the peroxisome proliferator-activated receptor, [PPAR]), transcriptomics is a primary endpoint for understanding the impact of the nutrient on biology. A simple example of the value of transcriptomics comes from the area of lipid metabolism. The synthesis of fatty acids and cholesterol is regulated by the sterol regulatory element-binding proteins (SREBP)-1a, -1c, and -2. Horton et al. (2003) used transcriptomics to study transgenic mice overexpressing each SREBP isoform and mice lacking all three nuclear SREBPs (i.e. in SREBP cleavage activating protein (SCAP)-deficient mice). They found several hundred transcripts that were changed in the liver and from this data they defined a subset of 33 genes as SREBP targets that included 20 new SREBP target genes. Thus, in one short, directed set of experiments they dramatically expanded our understanding of how lipid metabolism is regulated.

Proteomics

The proteome refers to all of the proteins expressed and functional in a system. Unfortunately, the methods to assess the proteome cannot measure the entire proteosome simultaneously. As a result, experiments usually measure one or more subproteomes, e.g. the phosphoproteome reflecting the proteins that are targets for protein kinases, proteins within subcellular compartments (e.g. mitochondrial proteome) or specific tissues (serum proteome), or proteins with specific physical properties (e.g. the membrane proteome).

There are two approaches to proteomics. The first is a “top-down” approach whereby whole proteins are studied using multidimensional separation techniques, e.g. separation by isoelectric point followed by size separation (2D polyacrylamide gel electrophoresis [PAGE]) or tandem mass spectrometry (Reid and McLuckey, 2002). In the top-down approach the proteins are isolated, then fragmented (e.g. through trypsin digestion), and the peptide fragments are compared with a database to determine the identity of the protein. In contrast, the “bottom-up” approach digests proteins at the outset, isolates and identifies the peptide fragments using mass spectrometry methods, and then relates the peptide fragments to databases of known proteins to determine the identity of the proteins in a complex mixture.

The major challenge to proteomics is that the spectrometry methods are not standardized. This leads to problems of reproducibility across and within labs. In addition, there are some challenges to separating signal from noise that limit peak detection and quantification. Finally, some proteomic methods are not very sensitive. Specifically, 2D PAGE approaches are often used for serum proteomics and biomarker discovery. However, the ability of radioimmunoassay to detect proteins in serum is 100- to 1000-fold greater than 2D PAGE methods. Even with this weakness, 2D PAGE has been useful for identifying serum biomarkers of nutritional status. For example, Fuchs et al. (2007) used this approach to identify biomarkers of a cardioprotective response to isoflavone supplementation in the peripheral blood mononuclear cells of postmenopausal women.

Metabolomics

Evaluating the metabolome gives a snapshot of the physiology of a cell or organism by simultaneously measuring the levels of metabolites within a biological space (also, see Chapter 4). Like proteomics, the metabolome is assessed by coupling separation techniques (e.g. electrophoresis, chromatography) with sophisticated detection methods (e.g. mass spectro­metry, nuclear magnetic resonance imaging). As such, it suffers from the same problems as proteomics, namely the lack of method standardization and reproducibility. Also, like proteomics, the entire metabolome is too complex for one method to measure all possible metabolites simultaneously, and so submetabolomes based on location or chemical characteristics are commonly analyzed. While many studies use metabolomics for biomarker discovery, this approach can also be used to better understand the impact of physiologic conditions on the flow of information through specific metabolic pathways. For example, in a study of diet-induced insulin resistance, Li et al. (2010) identified many serum and liver metabolites as different between safflower-oil-fed wild-type and glycerol-3-phosphate acyltransferase deficient mice. Many of these were not previously known to be associated with insulin resistance and they point to the utility of metabolomics analysis for identifying biochemical pathways important in understanding the pathophysiology of diabetes. In addition to the standard metabolomic approach that measured changes in steady-state levels of metabolites, others have used radio- or stable isotopes to label compounds and trace their metabolic fate. This is a more dynamic approach that can give a picture of how physiologic states or treatments affect the flow of compounds through specific metabolic pathways (Hellerstein, 2004).

Ionomics

Mineral elements are involved at all levels of biological regulation, e.g. in transcription factors (zinc), in enzymes (zinc, iron, copper, calcium), and in establishing electrochemical gradients in cells (calcium, sodium, potassium). It is also well established that direct and indirect interactions exist between mineral elements that can affect biology (Hill and Matrone, 1970). Because the mineral elements are integrated into the overall biology of a cell (i.e. with links to the metabolome, proteome, transcriptome, and ultimately the genome: et al.